Presently we have a mass of information available through the internet and this continues to grow at an unbelievably phenomenal rate. According to IBM 'Every day, we create 2.5 quintillion bytes of data - so much that 90% of the data in the world today has been created in the last two years alone'. This trend will only continue as projects to digitise the worlds libraries accelerate and the ‘Internet of things’ expands as an ever greater array of sensors add their data to the collective.
In the present paradigm the individual user is faced with the challenge of connecting with the information they require by a means of actively searching for it. The process begins with an individual recognising a need for information, developing appropriate search terms, initiating a search and then filtering and analysing the results as relevant or not to their search. This method works some of the time but relies on the individual imagining and then asking the right questions of the data that is available. This method works less effectively as the types of data available increases as the possible combinations of data expands exponentially with each new data source. We are rapidly approaching a point where the individual user is unlikely to imagine many of the possible uses of the data available to them.
What computer networks do best is the analysis of information through algorithmic means and matching of data sets. This power is what allows search engines to return the quality results we take for granted. The limiting factor is increasingly the carbon-based object at the keyboard.
But what if the network knew enough about you to initiate the search and present you with the most relevant results before you searched. This is what 'Google Now' does in matching what it knows about a user with the information the user provides from their search history, location data, calendar, email, contacts etc. While some find aspects of this creepy and a potential invasion of privacy the potential matching of personal information with immediate and relevant search has the potential to create many moments of serendipitous discovery. Apple is moving this way with Siri and Microsoft has Kortana both mimic in many respects a skilled Personal Assistant who is able to predict and meet the needs of their user.. Evernote has recently added ‘Context’ to their note taking platform and describes the service in similar terms.
Context is a new feature in Evernote. As you use Evernote, our Context algorithms try to find other information that is likely to be useful and relevant to whatever you’re working on. Our goal is to show you information that will help you improve the quality of your work, without you having to think about searching for it. It’s like having a super smart research assistant always by your side. - Learn More
Imagine a similar service integrated into your favourite word processor so that as you type it provides hints to relevant information and even automatically prompts citations to add evidence to your writing. Can we be far from a time when a student types  and the network does the rest?
Increasingly this model of 'data finding users' will occur in ways that are entirely friction free. New technologies such as ‘Google Glass’ or ‘Apple Watch’ will introduce a notification centric Operating System with a focus on bringing information to the user without the need for them to initiate the process. What if overtime the information provided was fine tuned as a result of minimal user feedback so that increasingly the network provided the information most needed at any moment without the need for a user-initiated action.
None of this involves a great leap in technology, only refinements of what is already available and the willingness to let it happen and to accept the perceived privacy implications of giving a network greater access to our personal data.
Now consider what this means for education and knowledge.
By Nigel Coutts